Nonoperative management (NOM) in rectal cancer: Physician perspectives on offering NOM as standard of care.
Bibliographic record
Abstract
79 Background: 20% of rectal cancer patients will have a complete clinical response (cCR) following neoadjuvant chemoradiotherapy. Non-operative management (NOM) with close surveillance can spare patients proctectomy and avoid the sequelae of surgery. Patients are interested in and advocate for NOM, whereas oncologists appear to be reluctant to offer this option. We wished to identify the perceptions and barriers that oncologists face when considering NOM. Methods: This qualitative study explored oncologists’ experiences treating rectal cancer and identified their perceptions and values around NOM. Purposive and snowball sampling identified medical, radiation and surgical oncologists’ who treat a high volume of rectal cancer across Canada. Oncologists varied in length/location of practice and gender. Data were collected via semi-structured interviews. Constant comparative analysis identified key concepts. Results: Data saturation was achieved after 40 interviews: 20 surgeons, 12 radiation and 8 medical oncologists. The dominant theme was “NOM is not ready for prime time’. Most oncologists felt that there is insufficient long-term data around NOM and single center studies appear ‘too good to be true’. Physicians voiced concerns about worsening oncologic outcomes in the setting of regrowth, the challenges in determining a cCR and apprehension around patient compliance to surveillance. Some oncologists felt that NOM is limited to a very select population and voiced reluctance in offering it to younger patients or patients with more advanced disease. There was little consideration to improved functional outcomes with NOM. Overall the majority of participants felt that NOM is ‘ trading the benefit of saving the rectum for the uncertainty of an inferior oncologic outcome’. Conclusions: Oncologists felt that NOM should not be offered as a standard of care option following a cCR. Most felt that there is insufficient data supporting NOM and are concerned around worse oncologic outcomes. Patient views of NOM are critically needed to assess if patients value the same outcomes. Additional research is needed to address barriers should patients wish to consider NOM as a treatment option in the setting of a cCR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".